
"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    @property
    def   _snake_case       (      self      :							str  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = (32, 32)

        _UpperCAmelCase        = floats_tensor((batch_size, num_channels) + sizes  ,       rng=random.Random(0  )  ).to(__UpperCamelCase  )
        return image







    @property
    def   _snake_case       (      self      :							List[str]  )		->List[Any]:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        return model







    @property
    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        return model







    @property
    def   _snake_case       (      self      :							Optional[Any]  )		->Any:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = RobertaSeriesConfig(
            hidden_size=32  ,       project_dim=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=50_06  ,       )
        return RobertaSeriesModelWithTransformation(__UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        def extract(*__UpperCamelCase      :							Any  ,       **__UpperCamelCase      :							Union[str, Any]  ):




            class 		a_    :







                def __init__(      self      :							Optional[int]  )		->int:






                    '''simple docstring'''






                    _UpperCAmelCase        = torch.ones([0]  )







                def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Union[str, Any]  )		->int:






                    '''simple docstring'''






                    self.pixel_values.to(__UpperCamelCase  )
                    return self







            return Out()

        return extract







    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase        = self.dummy_cond_unet
        _UpperCAmelCase        = PNDMScheduler(skip_prk_steps=__UpperCamelCase  )
        _UpperCAmelCase        = self.dummy_vae
        _UpperCAmelCase        = self.dummy_text_encoder
        _UpperCAmelCase        = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""  )
        _UpperCAmelCase        = 77

        _UpperCAmelCase        = self.dummy_image.to(__UpperCamelCase  )
        _UpperCAmelCase        = init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        _UpperCAmelCase        = AltDiffusionImgaImgPipeline(
            unet=__UpperCamelCase  ,       scheduler=__UpperCamelCase  ,       vae=__UpperCamelCase  ,       text_encoder=__UpperCamelCase  ,       tokenizer=__UpperCamelCase  ,       safety_checker=__UpperCamelCase  ,       feature_extractor=self.dummy_extractor  ,       )
        _UpperCAmelCase        = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,       do_normalize=__UpperCamelCase  )
        _UpperCAmelCase        = alt_pipe.to(__UpperCamelCase  )
        alt_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """A painting of a squirrel eating a burger"""
        _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )
        _UpperCAmelCase        = alt_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=6.0  ,       num_inference_steps=2  ,       output_type="""np"""  ,       image=__UpperCamelCase  ,       )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )
        _UpperCAmelCase        = alt_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=6.0  ,       num_inference_steps=2  ,       output_type="""np"""  ,       image=__UpperCamelCase  ,       return_dict=__UpperCamelCase  ,       )[0]

        _UpperCAmelCase        = image[0, -3:, -3:, -1]
        _UpperCAmelCase        = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCAmelCase        = np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 5e-3







    @unittest.skipIf(torch_device != """cuda"""  ,       """This test requires a GPU"""  )
    def   _snake_case       (      self      :							Optional[Any]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = self.dummy_cond_unet
        _UpperCAmelCase        = PNDMScheduler(skip_prk_steps=__UpperCamelCase  )
        _UpperCAmelCase        = self.dummy_vae
        _UpperCAmelCase        = self.dummy_text_encoder
        _UpperCAmelCase        = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""  )
        _UpperCAmelCase        = 77

        _UpperCAmelCase        = self.dummy_image.to(__UpperCamelCase  )

        # put models in fp16
        _UpperCAmelCase        = unet.half()
        _UpperCAmelCase        = vae.half()
        _UpperCAmelCase        = bert.half()

        # make sure here that pndm scheduler skips prk
        _UpperCAmelCase        = AltDiffusionImgaImgPipeline(
            unet=__UpperCamelCase  ,       scheduler=__UpperCamelCase  ,       vae=__UpperCamelCase  ,       text_encoder=__UpperCamelCase  ,       tokenizer=__UpperCamelCase  ,       safety_checker=__UpperCamelCase  ,       feature_extractor=self.dummy_extractor  ,       )
        _UpperCAmelCase        = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,       do_normalize=__UpperCamelCase  )
        _UpperCAmelCase        = alt_pipe.to(__UpperCamelCase  )
        alt_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """A painting of a squirrel eating a burger"""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = alt_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       num_inference_steps=2  ,       output_type="""np"""  ,       image=__UpperCamelCase  ,       ).images

        assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != """cuda"""  ,       """This test requires a GPU"""  )
    def   _snake_case       (      self      :							Optional[int]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""  )
        # resize to resolution that is divisible by 8 but not 16 or 32
        _UpperCAmelCase        = init_image.resize((7_60, 5_04)  )

        _UpperCAmelCase        = """BAAI/AltDiffusion"""
        _UpperCAmelCase        = AltDiffusionImgaImgPipeline.from_pretrained(
            __UpperCamelCase  ,       safety_checker=__UpperCamelCase  ,       )
        pipe.to(__UpperCamelCase  )
        pipe.set_progress_bar_config(disable=__UpperCamelCase  )
        pipe.enable_attention_slicing()

        _UpperCAmelCase        = """A fantasy landscape, trending on artstation"""

        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = pipe(
            prompt=__UpperCamelCase  ,       image=__UpperCamelCase  ,       strength=0.7_5  ,       guidance_scale=7.5  ,       generator=__UpperCamelCase  ,       output_type="""np"""  ,       )
        _UpperCAmelCase        = output.images[0]

        _UpperCAmelCase        = image[2_55:2_58, 3_83:3_86, -1]

        assert image.shape == (5_04, 7_60, 3)
        _UpperCAmelCase        = np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2







@slow
@require_torch_gpu
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							List[str]  )		->Dict:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""  )
        _UpperCAmelCase        = init_image.resize((7_68, 5_12)  )
        _UpperCAmelCase        = load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""  )

        _UpperCAmelCase        = """BAAI/AltDiffusion"""
        _UpperCAmelCase        = AltDiffusionImgaImgPipeline.from_pretrained(
            __UpperCamelCase  ,       safety_checker=__UpperCamelCase  ,       )
        pipe.to(__UpperCamelCase  )
        pipe.set_progress_bar_config(disable=__UpperCamelCase  )
        pipe.enable_attention_slicing()

        _UpperCAmelCase        = """A fantasy landscape, trending on artstation"""

        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = pipe(
            prompt=__UpperCamelCase  ,       image=__UpperCamelCase  ,       strength=0.7_5  ,       guidance_scale=7.5  ,       generator=__UpperCamelCase  ,       output_type="""np"""  ,       )
        _UpperCAmelCase        = output.images[0]

        assert image.shape == (5_12, 7_68, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image  ).max() < 1e-2

"""simple docstring"""



import warnings
from functools import wraps
from typing import Callable


def 			_UpperCamelCase						(    _A   )     ->	Callable:





    """simple docstring"""

    @wraps(_A   )
    def _inner_fn(*_A       ,   **_A   ):
        warnings.warn(
            (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")       ,   _A       ,   )
        return fn(*_A       ,   **_A   )

    return _inner_fn